Curr Treat Options Gastroenterol. 2020 Jan 21. doi: 10.1007/s11938-020-00274-2. [Epub ahead of print]
Artificial Intelligence and Polyp Detection.
Abstract PURPOSE OF REVIEW: This review highlights the history, recent advances, and ongoing challenges of artificial intelligence (AI) technology in colonic polyp detection. RECENT FINDINGS: Hand-crafted AI algorithms have recently given way to convolutional neural networks with the ability to detect polyps in real-time. The first randomized controlled trial comparing an AI system to standard colonoscopy found a 9% increase in adenoma detection rate, but the improvement was restricted to polyps smaller than 10 mm and the results need validation. As this field rapidly evolves, important issues to consider include standardization of outcomes, dataset availability, real-world applications, and regulatory approval. AI has shown great potential for improving colonic polyp detection while requiring minimal training for endoscopists. The question of when AI will enter endoscopic practice depends on whether the technology can be integrated into existing hardware and an assessment of its added value for patient care.
KEYWORDS: Artificial intelligence; Colonic neoplasm; Computer-aided detection; Convolutional neural network; Machine learning